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Single_sim.py
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Single_sim.py
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# We separated parts of the Original SimulationPipeline.py
# It's cause we don't really want to change SimulationPiepline.py too much.
# In this file, we define the Parameter Dictionary
# As well as set up the looping.
# For now we do the looping thorugh pipeline instead of through the PBS array as I don't get it.
# First Import the packages.
import numpy as np
import pandas as pd
import pylab
import matplotlib.pyplot as plt
from scipy import stats
import inspect
import os
import csv
import time
import sys
from tvb.simulator.lab import *
from tvb.simulator.plot.tools import *
# Input Simulation Pipeline
from SimulationPipeline import *
#from useful_fns import *
################################################################################################################################
# These are Default Values for ParamsDict.
# Empty dict
ParamsDict = { }
# Name of import file/zip - Which contains connectivity data.
ParamsDict["name"] = "MouseCortex"
# Calculate FC-FC for Mouse?
ParamsDict["FCFC"] = True
# Export Simulation Files?
ParamsDict["ExportSim"] = True
# Monitors or not? (Aka BOLD or not?)
ParamsDict["BOLD"] = False
# Change to Binary Connectome? (If True, will change the connectome into binary)
ParamsDict["BINARY"] = True
# Snip is the the number of elements you wish to snip at the start to remove the initial condition effects.
ParamsDict["Snip"] = 10
# Note, if BOLD = False, Snip gets multiplied by 100, later in the SimulationPipeline code. Not the actual dictionary element though.
# Set the Random State/Seed for the Stochastic Integrator:
ParamsDict["RandState"] = 118
# Remove ith indexed region (7 corresponds to Frontal Pole Cerebral Cortex) - Give it a list if removing multiple regions. Empty list removes nothing.
ParamsDict["REMOVE"] = [7]
# Set Simulation Length:
ParamsDict["Simul_length"] = 1.2e5
# Set Linear Coupling Constant:
ParamsDict["G"] = np.array([0.47])
# Set integrator time step dt.
ParamsDict["dt"] = 0.1
# Set Additive Noise strength
ParamsDict["noise"] = np.array([0.000013])
# Set Initial Conditions. Comment out if you want TVB to make initial conditions for you. Init_Cons CANT be in the Dict in this case.
#ParamsDict["Init_Cons"] = np.array()
#initial_conditions=0.5 + numpy.zeros((con.number_of_regions*con.number_of_regions,2,con.number_of_regions,1))
# Set Wilson Cowan Model Parameters - Limit Cycle Cut
ParamsDict["MODEL_c_ee"] = np.array([11.0])
ParamsDict["MODEL_c_ei"] = np.array([10.0])
ParamsDict["MODEL_c_ie"] = np.array([10.0])
ParamsDict["MODEL_c_ii"] = np.array([1.0])
# Model is now defined within SimulationPipeline.py
# However if you adjusting parameters other than these Coupling Parameters, then you need to redefine the model in this file per run.
ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
a_e=numpy.array([1.0]),a_i=numpy.array([1.0]),b_e=numpy.array([1.5]),b_i=numpy.array([2.8]),tau_e=numpy.array([10.0]),
tau_i=numpy.array([65.0]))
# Params Dict tag (extra note tags for the name - Example to denote what's being changed/looped.)
ParamsDict["tag"] = ""
################################################################################################################################
# Set random states
# ParamsDict["RandState"] = i * 12 + 1
df = pd.read_csv("CortexDensitiesAlter.csv",delimiter=",")
E_pop = df.excitatory.values
I_pop = df.inhibitory.values
E_mean = np.mean(E_pop)
I_mean = np.mean(I_pop)
# E_normalised is (when excluding region 7) -0.28 to 0.54
E_normalised = (E_pop-E_mean)/E_mean
# I_normalised is (when excluding region 7) -0.45 to 1.44
I_normalised = (I_pop-I_mean)/I_mean
'''
# Simulate everything Homogeneous Sweeps for Simulation Length 1.2e6
ParamsDict["Simul_length"] = 1.2e6
ParamsDict["Snip"] = 100 # Gets multiplied by 100 so this is 1 e4 (or 1%)
'''
# Simulate LCycle Het 1.2 e5 no G sweep and finer sigma.
# LCycle
ParamsDict["MODEL_c_ee"] = np.array([11.0])
ParamsDict["MODEL_c_ei"] = np.array([10.0])
ParamsDict["MODEL_c_ie"] = np.array([10.0])
ParamsDict["MODEL_c_ii"] = np.array([1.0])
ParamsDict["B_e"] = np.array([1.5])
ParamsDict["G"] = np.array([0.7])
# Homogeneous Coupling constants
h_ee = ParamsDict["MODEL_c_ee"]
h_ei = ParamsDict["MODEL_c_ei"]
h_ie = ParamsDict["MODEL_c_ie"]
h_ii = ParamsDict["MODEL_c_ii"]
for I in np.arange(11):
ParamsDict["Sigma_e"] =I*0.1
sigma_e = ParamsDict["Sigma_e"]
for J in np.arange(11):
ParamsDict["Sigma_i"] = J*0.1
sigma_i = ParamsDict["Sigma_i"]
# Heterogeneous Coupling Constants (array)
ParamsDict["MODEL_c_ie"] = h_ie * (1 + sigma_e * E_normalised)
ParamsDict["MODEL_c_ee"] = h_ee * (1 + sigma_e * E_normalised)
ParamsDict["MODEL_c_ii"] = h_ii * (1 + sigma_i * I_normalised)
ParamsDict["MODEL_c_ei"] = h_ei * (1 + sigma_i * I_normalised)
ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
a_e=numpy.array([1.0]),a_i=numpy.array([1.0]),b_e=ParamsDict["B_e"],b_i=numpy.array([2.8]),tau_e=numpy.array([10.0]),
tau_i=numpy.array([65.0]))
ParamsDict["tag"] = "LCycle_G" + str(ParamsDict["G"]) + "_b_e" + str(ParamsDict["B_e"]) + "_sig_e" + str(sigma_e) + "_sig_i" + str(sigma_i)
Simul_Pipeline(ParamsDict=ParamsDict)
"""
# i is PBS_ARRAY_INDEX - Allows for creation of multiple jobs
i = int(sys.argv[1])
ParamsDict["G"] = np.array([i*0.05])
df = pd.read_csv("CortexDensitiesAlter.csv",delimiter=",")
E_pop = df.excitatory.values
I_pop = df.inhibitory.values
E_mean = np.mean(E_pop)
I_mean = np.mean(I_pop)
# E_normalised is (when excluding region 7) -0.28 to 0.54
E_normalised = (E_pop-E_mean)/E_mean
# I_normalised is (when excluding region 7) -0.45 to 1.44
I_normalised = (I_pop-I_mean)/I_mean
# Set Wilson Cowan Model Parameters - LCycleH - LCycle Parameters close to Hysteresis Case.
ParamsDict["MODEL_c_ee"] = np.array([16.0])
ParamsDict["MODEL_c_ei"] = np.array([12.0])
ParamsDict["MODEL_c_ie"] = np.array([15.0])
ParamsDict["MODEL_c_ii"] = np.array([3.0])
# Homogeneous Coupling constants
h_ee = ParamsDict["MODEL_c_ee"]
h_ei = ParamsDict["MODEL_c_ei"]
h_ie = ParamsDict["MODEL_c_ie"]
h_ii = ParamsDict["MODEL_c_ii"]
for J in np.arange(6):
ParamsDict["sigma"] =J*0.2
sigma = ParamsDict["sigma"]
# Heterogeneous Coupling Constants (array)
ParamsDict["MODEL_c_ie"] = h_ie * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ee"] = h_ee * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ii"] = h_ii * (1 + sigma * I_normalised)
ParamsDict["MODEL_c_ei"] = h_ei * (1 + sigma * I_normalised)
ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
a_e=numpy.array([1.3]),a_i=numpy.array([2.0]),b_e=numpy.array([4]),b_i=numpy.array([3.7]),tau_e=numpy.array([10.0]),
tau_i=numpy.array([10.0]))
ParamsDict["tag"] = "LCycleH_G" + str(ParamsDict["G"])
Simul_Pipeline(ParamsDict=ParamsDict)
# Set Wilson Cowan Model Parameters - LCycleHCut - LCycleCut Parameters close to Hysteresis Case, but chopped so hopf bifurcation.
ParamsDict["MODEL_c_ee"] = np.array([16.0])
ParamsDict["MODEL_c_ei"] = np.array([12.0])
ParamsDict["MODEL_c_ie"] = np.array([15.0])
ParamsDict["MODEL_c_ii"] = np.array([3.0])
# Homogeneous Coupling constants
h_ee = ParamsDict["MODEL_c_ee"]
h_ei = ParamsDict["MODEL_c_ei"]
h_ie = ParamsDict["MODEL_c_ie"]
h_ii = ParamsDict["MODEL_c_ii"]
for J in np.arange(6):
ParamsDict["sigma"] =J*0.2
sigma = ParamsDict["sigma"]
# Heterogeneous Coupling Constants (array)
ParamsDict["MODEL_c_ie"] = h_ie * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ee"] = h_ee * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ii"] = h_ii * (1 + sigma * I_normalised)
ParamsDict["MODEL_c_ei"] = h_ei * (1 + sigma * I_normalised)
ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
a_e=numpy.array([1.3]),a_i=numpy.array([2.0]),b_e=numpy.array([3]),b_i=numpy.array([3.7]),tau_e=numpy.array([10.0]),
tau_i=numpy.array([10.0]))
ParamsDict["tag"] = "LCycleH_G" + str(ParamsDict["G"])
Simul_Pipeline(ParamsDict=ParamsDict)
"""
"""
# Set Wilson Cowan Model Parameters - Hysteresis
ParamsDict["MODEL_c_ee"] = np.array([16.0])
ParamsDict["MODEL_c_ei"] = np.array([12.0])
ParamsDict["MODEL_c_ie"] = np.array([10.0])
ParamsDict["MODEL_c_ii"] = np.array([3.0])
# Homogeneous Coupling constants
h_ee = ParamsDict["MODEL_c_ee"]
h_ei = ParamsDict["MODEL_c_ei"]
h_ie = ParamsDict["MODEL_c_ie"]
h_ii = ParamsDict["MODEL_c_ii"]
# Hysteresis
for J in np.arange(6):
ParamsDict["sigma"] =J*0.2
sigma = ParamsDict["sigma"]
# Heterogeneous Coupling Constants (array)
ParamsDict["MODEL_c_ie"] = h_ie * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ee"] = h_ee * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ii"] = h_ii * (1 + sigma * I_normalised)
ParamsDict["MODEL_c_ei"] = h_ei * (1 + sigma * I_normalised)
ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
a_e=numpy.array([1.3]),a_i=numpy.array([2.0]),b_e=numpy.array([4]),b_i=numpy.array([3.7]),tau_e=numpy.array([10.0]),
tau_i=numpy.array([10.0]))
ParamsDict["tag"] = "Hysteresis_G" + str(ParamsDict["G"])
Simul_Pipeline(ParamsDict=ParamsDict)
# FixedPt-eqbm
# Set Wilson Cowan Model Parameters - Hysteresis
ParamsDict["MODEL_c_ee"] = np.array([12.0])
ParamsDict["MODEL_c_ei"] = np.array([15.0])
ParamsDict["MODEL_c_ie"] = np.array([10.0])
ParamsDict["MODEL_c_ii"] = np.array([8.0])
# Homogeneous Coupling constants
h_ee = ParamsDict["MODEL_c_ee"]
h_ei = ParamsDict["MODEL_c_ei"]
h_ie = ParamsDict["MODEL_c_ie"]
h_ii = ParamsDict["MODEL_c_ii"]
for J in np.arange(6):
ParamsDict["sigma"] =J*0.2
sigma = ParamsDict["sigma"]
# Heterogeneous Coupling Constants (array)
ParamsDict["MODEL_c_ie"] = h_ie * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ee"] = h_ee * (1 + sigma * E_normalised)
ParamsDict["MODEL_c_ii"] = h_ii * (1 + sigma * I_normalised)
ParamsDict["MODEL_c_ei"] = h_ei * (1 + sigma * I_normalised)
ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
a_e=numpy.array([1.0]),a_i=numpy.array([1.0]),b_e=numpy.array([4]),b_i=numpy.array([4]),tau_e=numpy.array([10.0]),
tau_i=numpy.array([10.0]))
ParamsDict["tag"] = "FixedPt_G" + str(ParamsDict["G"])
Simul_Pipeline(ParamsDict=ParamsDict)
"""
"""
# Limit Cycle Params - LCycleCut
ParamsDict["MODEL_c_ee"] = np.array([11.0])
ParamsDict["MODEL_c_ei"] = np.array([10.0])
ParamsDict["MODEL_c_ie"] = np.array([10.0])
ParamsDict["MODEL_c_ii"] = np.array([1.0])
# Homogeneous Coupling constants
h_ee = ParamsDict["MODEL_c_ee"]
h_ei = ParamsDict["MODEL_c_ei"]
h_ie = ParamsDict["MODEL_c_ie"]
h_ii = ParamsDict["MODEL_c_ii"]
ParamsDict["MODEL"] = models.WilsonCowan(c_ee=ParamsDict["MODEL_c_ee"],c_ei=ParamsDict["MODEL_c_ei"],c_ie=ParamsDict["MODEL_c_ie"] ,c_ii=ParamsDict["MODEL_c_ii"],
a_e=numpy.array([1.0]),a_i=numpy.array([1.0]),b_e=numpy.array([1.5]),b_i=numpy.array([2.8]),tau_e=numpy.array([10.0]),
tau_i=numpy.array([65.0]))
ParamsDict["tag"] = "LCycleCut_G" + str(ParamsDict["G"])
Simul_Pipeline(ParamsDict=ParamsDict)
"""